Assessing Student Success: The Impact of Machine Learning and XAI-BBO Approach

Abstract

In the study conducted to analyze the factors affecting student success in education, various preprocessing steps were applied to the dataset, and transformations aimed at effectively utilizing categorical variables were particularly implemented. These transformations included factors such as students' gender, age range, and parental education level. Subsequently, the Biogeography-Based Optimization (BBO) algorithm was utilized to determine the most important 20 features, which were then incorporated into machine learning models. During the evaluation phase, metrics such as Accuracy, Precision, Recall, and F1 score were employed to obtain results. The highest Accuracy value, 0.7388, was achieved with the Gradient Boosting algorithm. To elucidate the success of this algorithm, interpretable artificial intelligence models such as SHAP and LIME methods were employed. The findings of the study underscored the importance of detailed examination of factors influencing student success, emphasizing the need for further research to formulate education policies more effectively. The results of this study may contribute to the enhancement of data-driven decision-making processes in education and the more effective planning of interventions aimed at improving student success.